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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from ..local_opt import StochasticHillClimbingOptimizer
class SimulatedAnnealingOptimizer(StochasticHillClimbingOptimizer):
name = "Simulated Annealing"
_name_ = "simulated_annealing"
__name__ = "SimulatedAnnealingOptimizer"
optimizer_type = "local"
computationally_expensive = False
def __init__(self, *args, annealing_rate=0.97, start_temp=1, **kwargs):
super().__init__(*args, **kwargs)
self.annealing_rate = annealing_rate
self.start_temp = start_temp
self.temp = start_temp
def _p_accept_default(self):
# the 'minus' is omitted, because we maximize a score
return np.exp(self._exponent)
def evaluate(self, score_new):
StochasticHillClimbingOptimizer.evaluate(self, score_new)
self.temp *= self.annealing_rate
SimonBlanke /
Gradient-Free-Optimizers
